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Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges

Lonza

€29.99

3 (3 Ratings)

Paperback Oct 2019 366 pages 1st Edition

Lonza

€29.99

3 (3 Ratings)

Paperback Oct 2019 366 pages 1st Edition

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Key benefits

Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks
Understand and develop model-free and model-based algorithms for building self-learning agents
Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategies

Description

Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents. Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS. By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.

Who is this book for?

If you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You’ll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.

What you will learn

Develop an agent to play CartPole using the OpenAI Gym interface
Discover the model-based reinforcement learning paradigm
Solve the Frozen Lake problem with dynamic programming
Explore Q-learning and SARSA with a view to playing a taxi game
Apply Deep Q-Networks (DQNs) to Atari games using Gym
Study policy gradient algorithms, including Actor-Critic and REINFORCE
Understand and apply PPO and TRPO in continuous locomotion environments
Get to grips with evolution strategies for solving the lunar lander problem

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What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Estimated delivery fee Deliver to Netherlands

Premium delivery 7 - 10 business days

€17.95

(Includes tracking information)

Product Details

Publication date : Oct 18, 2019

Length: 366 pages

Edition : 1st

Language : English

ISBN-13 : 9781789131116

Category :

Data

Languages :

Python

Concepts :

Reinforcement Learning

Tools :

OpenAI Gym

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18 Chapters

Section 1: Algorithms and Environments

The Landscape of Reinforcement Learning

An introduction to RL

Implementing RL Cycle and OpenAI Gym

Setting up the environment

OpenAI Gym and RL cycles

Development of ML models using TensorFlow

Introducing TensorBoard

Types of RL environments

Summary

Questions

Further reading

Solving Problems with Dynamic Programming

MDP

Categorizing RL algorithms

Section 2: Model-Free RL Algorithms

Q-Learning and SARSA Applications

Learning without a model

TD learning

SARSA

Applying SARSA to Taxi-v2

Q-learning

Applying Q-learning to Taxi-v2

Summary

Questions

Deep Q-Network

Deep neural networks and Q-learning

Learning Stochastic and PG Optimization

Policy gradient methods

Understanding the REINFORCE algorithm

REINFORCE with baseline

Learning the AC algorithm

Summary

Questions

Further reading

TRPO and PPO Implementation

Roboschool

Natural policy gradient

Trust region policy optimization

Proximal Policy Optimization

Summary

Questions

Further reading

DDPG and TD3 Applications

Combining policy gradient optimization with Q-learning

Deep deterministic policy gradient

Twin delayed deep deterministic policy gradient (TD3)

Summary

Questions

Further reading

Section 3: Beyond Model-Free Algorithms and Improvements

Model-Based RL

Model-based methods

Combining model-based with model-free learning

ME-TRPO applied to an inverted pendulum

Summary

Questions

Further reading

Imitation Learning with the DAgger Algorithm

Technical requirements

The imitation approach

Playing Flappy Bird

Understanding the dataset aggregation algorithm

Understanding Black-Box Optimization Algorithms

Beyond RL

The core of EAs

Scalable evolution strategies

Applying scalable ES to LunarLander

Summary

Questions

Further reading

Developing the ESBAS Algorithm

Exploration versus exploitation

Approaches to exploration

Epochal stochastic bandit algorithm selection

Summary

Questions

Further reading

Practical Implementation for Resolving RL Challenges

Best practices of deep RL

Challenges in deep RL

Advanced techniques

RL in the real world

Future of RL and its impact on society

Summary

Questions

Further reading

Assessments

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Customer reviews

Rating distribution

(3 Ratings)

5 star 33.3%

4 star 0%

3 star 33.3%

2 star 0%

1 star 33.3%

Sebastien Feb 16, 2020

Easy to understand and in each chapter there is an example, with code, that help you understand how to actually use the algorithm

Amazon Verified review

schwarzwald Apr 12, 2020

Good content but doesn't mention that Tensorflow 1 is required. The book uses code that will not work in the current TF version. For people not intimately familiar with TF 1 vs. 2, this will be confusing.

Amazon Verified review

designer Mar 16, 2023

Non of the code samples in the book and code samples in GitHub work because they are outdated. The code showed 4 years ago and the author hasn’t updated them. When I copied the samples, they all kept saying outdated and a bunch of other errors due to solfware incompatible. Wasted time and money

Amazon Verified review

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About the author

Lonza

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.

See other products by Lonza

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Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges

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